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141 results for “ecosystem dynamics”
Data from: Possible adverse impact of contaminants on Atlantic cod population dynamics in coastal ecosystems
While many in-lab ecotoxicological studies have shown the adverse impact of pollutants to the fitness of an individual, direct evidence from the field on the population dynamics of wildlife animals has been lacking. Here, we provide empirical support for a negative effect of pollution on Atlantic cod (Gadus morhua) population dynamics in coastal waters of Norway by combining unique time series of juvenile cod abundance, body size, environmental concentration of toxic contaminants, and a spatially structured population dynamics model. The study shows that mercury concentration might have decreased the reproductive potential of cod in the region despite the general decline in the environmental concentration of mercury, cadmium, and hexachlorobenzene since the implementation of national environmental laws. However, some cod populations appeared to be more resistant to mercury pollution than others and the strength and shape of mercury effect on cod reproductive potential was fjord-specific. Additionally, cod growth rate changed at scales smaller than fjords with a gradient related to the exposure to the open ocean and offshore cod. These spatial differences in life history traits emphasize the importance of local adaptation in shaping the dynamics of local wildlife populations. Finally, this study highlights the possibility to mitigate pollution effects on natural population by reducing the overall pollution level but also reveals that pollution reduction alone is not enough to rebuild local cod populations. Cod population recovery probably requires complimentary efforts on fishing regulation and habitat restauration.
Data from: Analysing the dynamics and relative influence of variables affecting ecosystem responses using functional PCA and boosted trees: a seagrass case study
1. Understanding the relative influence of variables on ecosystem responses and the dynamics of their effect is necessary for effective ecosystem monitoring and management. Also known as causal pathways anlaysis, we develop an approach using functional Principal Components Analysis (fPCA) and machine learning within a scenario analysis framework. 2. fPCA is used to identify most influential variables for correlated, non-homogenoeus and non-linear time series data characteristic of complex ecosystems. Hierarchical clustering of fPCA scores reveals groups of more homogeneous scenarios and similarly influential variables. The resultant subset of variables helps to overcome model identifiability problems when analysing time-lagged effects using Boosted Regression Trees (BRT). 3. We use simulated data generated by a Dynamic Bayesian Network (DBN) of ecological windows for seagrass ecosystems given dredging stressors; 3024 scenarios with 75 state variables are analysed. The BRT demonstrated a high level of fit ((R^2≈0.97,MSE≈0.16), supporting the validity of influential variables identified by fPCA. Influential variables identified included genus, location type, light, growth and seed. Six consecutive months of positive growth and adequate light were important for predicting states of high or moderate population. 4. Compared to traditional scenario analysis and sensitivity analysis approaches, our approach simultaneously enabled capture of n-way interactions while accounting for time correlations. Although some variables and their dynamics agreed with existing knowledge, new variables and/or time lags of their effects were identified, corresponding to opportunities for further investigation as well as informing monitoring and management. Although our method was demonstrated on state variables with DBN simulated data, it is equally applicable to general time series data.
Data from: Spatial and successional dynamics of microbial biofilm communities in a grassland stream ecosystem
Biofilms represent a metabolically active and structurally complex component of freshwater ecosystems. Ephemeral prairie streams are hydrologically harsh and prone to frequent perturbation. Elucidating both functional and structural community changes over time within prairie streams provides a general understanding of microbial responses to environmental disturbance. We examined microbial succession of biofilm communities at three sites in a third-order stream at Konza Prairie over a 2- to 64-day period. Microbial abundance (bacterial abundance, chlorophyll a concentrations) increased and never plateaued during the experiment. Net primary productivity (net balance of oxygen consumption and production) of the developing biofilms did not differ statistically from zero until 64 days suggesting a balance of the use of autochthonous and allochthonous energy sources until late succession. Bacterial communities (MiSeq analyses of the V4 region of 16S rRNA) established quickly. Bacterial richness, diversity and evenness were high after 2 days and increased over time. Several dominant bacterial phyla (Beta-, Alphaproteobacteria, Bacteroidetes, Gemmatimonadetes, Acidobacteria, Chloroflexi) and genera (Luteolibacter, Flavobacterium, Gemmatimonas, Hydrogenophaga) differed in relative abundance over space and time. Bacterial community composition differed across both space and successional time. Pairwise comparisons of phylogenetic turnover in bacterial community composition indicated that early-stage succession (≤16 days) was driven by stochastic processes, whereas later stages were driven by deterministic selection regardless of site. Our data suggest that microbial biofilms predictably develop both functionally and structurally indicating distinct successional trajectories of bacterial communities in this ecosystem.
Supplementary material 1 from: Sogawa S, Tsuchiya K, Nagai S, Shimode S, Kuwahara VS (2022) Annual dynamics of eukaryotic and bacterial communities revealed by 18S and 16S rRNA metabarcoding in the coastal ecosystem of Sagami Bay, Japan. Metabarcoding and Metagenomics 6: e78181. https://doi.org/10.3897/mbmg.6.78181
Figures S1–S8
Supplementary material 7 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
R Script for converting of microclimatic data
Supplementary material 1 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Detailed design of the Field Experiment
Supplementary material 2 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Brief description of the Field Experiment
Figure 1 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Figure 1 A. Conceptual diagram of the mechanistic approach of the planned Research Unit. B. Conceptual scheme of the proposed evolutionary niche shifts in plant monocultures and mixtures. This idea feeds into our understanding of how evolutionary history influences the ecological interactions of species that compete for growth factors, ultimately defining biotope space (gray rectangle; Hutchinson 1978). Graphically depicted, species (ellipses) in mixture will show increasing niche differentiation over time due to competition (niche overlap). Thus, history of selection in diverse communities is expected to result in greater interspecific differences (less overlap of ellipses) and more specialization (smaller ellipses) than a history of isolation (monocultures). In monocultures, species will experience strong selection pressure by accumulating soil-borne pathogens, and species may invest energy in chemical and morphological defense traits (depicted by ellipses shifting towards the same corner of the habitat space). Plants in mixtures together may exploit more available biotope space than single monocultures, causing increasing diversity effects on ecosystem functions over time. However, there is limited support for this assumption for traits related to light (e.g., Lipowsky et al. 2015, Roscher et al. 2015) and resource use (Jesch et al. 2018) so far.
Figure 4 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Figure 4 Experimental design and hypotheses of the Ecotron Experiment. Briefly, four treatments will be established based on monoliths from a selection of the 9-year old Trait-Based Experiment (TBE; Ebeling et al. 2014) and from bare ground plots of the Jena Experiment as well as two seed sources: the respective plots and the original seed material that was used for the set-up of the TBE. (1) With plot-specific plant history and with plot-specific soil history; (2) without plot-specific plant history and with plot-specific soil history; (3) with plot-specific plant history and without plot-specific soil history; and (4) without plot-specific plant history and without plot-specific soil history. We expect the biodiversity–ecosystem function relationships to differ among the four treatments (see main text for details).
Figure 3 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Figure 3 Hypothesized slope of BEF relationships in the different treatments of the Field Experiment (see main text for details). Note that the 'with plant history, with soil history' only serves as a control in the Field Experiment, and effects of plant history can only be tested in the planned Ecotron Experiment. Redrawn after Vogel et al. (2019). '+', with; '-', without.
Supplementary material 4 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Detailed design of the Ecotron Experiment
Supplementary material 5 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Brief description of the Ecotron Experiment
Figure 2 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Figure 2 Structure of the proposed Research Unit. Three complementary experimental approaches are envisaged to study long-term biodiversity-ecosystem function (BEF) relationships, and how these are influenced by plant history and soil history. BEF patterns are studied in the Field Experiment with long-term plant diversity plots and manipulations of soil-history effects. BEF mechanisms are studied in the Ecotron Experiment and in Microcosm Experiments. In the Ecotron Experiment, plant history and soil history are independently crossed and detailed process measurements are possible. The Microcosm Experiments zoom in on focal interactions. In the Field Experiment and in the Ecotron Experiment, studies are conducted at the community level as well as at the plant individual level (magnifier; see detailed design of studies in the Appendices). Subprojects' (SPs') participation in experiments are illustrated with lines. The SPs of the proposed Research Unit fall into two tightly linked main categories (in gray) with two research areas each that aim at exploring variation in community assembly processes, micro-evolutionary changes, and resulting differences in biotic interactions as determinants of the long-term BEF relationship. Subprojects under "Microbial community assembly" (blue) and "Assembly and functions of animal communities" (red) mostly focus on plant diversity effects on the assembly of communities and their feedback effects on biotic interactions and ecosystem functions, while subprojects under "Mediators of plant-biotic interactions" (orange) and "Intraspecific diversity and micro-evolutionary changes" (green) mostly focus on plant diversity effects on plant trait expression and micro-evolution. PIs with requested personnel are underlined.
Supplementary material 3 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Plant species lists of the Field Experiment and the Ecotron Experiment
Supplementary material 3 from: Ariza GM, Jácome J, Esquivel HE, Kotze DJ (2021) Early successional dynamics of ground beetles (Coleoptera, Carabidae) in the tropical dry forest ecosystem in Colombia. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 877-906. https://doi.org/10.3897/zookeys.1044.59475
Table S3
Figure 1 from: Ariza GM, Jácome J, Esquivel HE, Kotze DJ (2021) Early successional dynamics of ground beetles (Coleoptera, Carabidae) in the tropical dry forest ecosystem in Colombia. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 877-906. https://doi.org/10.3897/zookeys.1044.59475
Figure 1 Geographic location of the study sites A the location of Armero and Cambao in Colombia B Armero C Cambao. Abbreviations: F = forest, ES = early succession, P = pasture. Maps courtesy of DIVA-GIS 7.5 and Google Earth Image 2020. For more details, see the online map at https://www.google.com/maps/d/u/3/edit?mid=1le-kQOQFh8RumUibWP3D8ghtxVvGM-eF&usp=sharing
Figure 3 from: Ariza GM, Jácome J, Esquivel HE, Kotze DJ (2021) Early successional dynamics of ground beetles (Coleoptera, Carabidae) in the tropical dry forest ecosystem in Colombia. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 877-906. https://doi.org/10.3897/zookeys.1044.59475
Figure 3 Non-metric multidimensional scaling ordination of carabid beetle assemblages at Armero (Colombia). Wet and dry season catches were analyzed and plotted separately. The catch in five of the ten forest samples returned zero individuals, and were removed from the analysis. The ellipses indicate 1 SD of the weighted average of site scores of forest (dotted line), early succession (long dashed line), and pasture (solid line). Abbreviations of the significant environmental vectors: soiltemp = soil temperature, airtemp = air temperature, litterdepth = leaf litter depth (cm), canopy = percentage canopy cover. Stress value 0.06.
Supplementary material 2 from: Ariza GM, Jácome J, Esquivel HE, Kotze DJ (2021) Early successional dynamics of ground beetles (Coleoptera, Carabidae) in the tropical dry forest ecosystem in Colombia. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 877-906. https://doi.org/10.3897/zookeys.1044.59475
Table S2
Figure 5 from: Ariza GM, Jácome J, Esquivel HE, Kotze DJ (2021) Early successional dynamics of ground beetles (Coleoptera, Carabidae) in the tropical dry forest ecosystem in Colombia. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 877-906. https://doi.org/10.3897/zookeys.1044.59475
Figure 5 Generalized Linear Mixed Model predicted (mean ± SE) number of individuals of Calosoma alternans, genus Megacephala and the remaining carabid beetle species collected from Armero and Cambao combined across the three habitat types (forest, early succession, and pasture). Note different y-axis scales.
Supplementary material 1 from: Ariza GM, Jácome J, Esquivel HE, Kotze DJ (2021) Early successional dynamics of ground beetles (Coleoptera, Carabidae) in the tropical dry forest ecosystem in Colombia. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 877-906. https://doi.org/10.3897/zookeys.1044.59475
Table S1
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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